Objective of Decision Analysis. Determine an optimal decision under uncertain future events
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1 Decision Analysis
2 Objective of Decision Analysis Determine an optimal decision under uncertain future events
3 Formulation of Decision Problem Clear statement of the problem Identify: The decision alternatives The future events that could impact the decision. These are referred to as chance events The consequences: the outcomes associated with each decision alternative and chance event
4 Example: PDC Constructing Luxury Condominium Complex Problem: How large should the complex be PDC limited the options to: d 1 = small complex with 30 condominiums d 2 = medium complex with 60 condominiums d 3 = large complex with 90 condominiums Future chance events were limited to: s 1 = strong demand for the condominiums s 2 = weak demand for the condominiums
5 Representation of the Decision Problem Payoff Tables Decision Tree
6 Payoff Table State of Nature Decision Alternative Strong Demand s 1 Weak Demand s 2 Small Complex, d Medium Complex, d Large Complex, d
7 Decision Tree
8 Decision Making without Probabilities Optimistic Approach Conservative Approach Minimax Regret Approach
9 Optimistic Approach Select the highest payoff (for maximization) or lowest payoff (for minimization) Decision Alternative Maximum Payoff Small Complex, d 1 8 Medium Complex, d 2 14 Large Complex, d 3 20 Maximum of the maximum payoffs
10 Conservative Approach Select the maximum of the minimum payoffs Decision Alternative Minimum Payoff Small Complex, d 1 7 Medium Complex, d 2 5 Maximum of the minimum payoffs Large Complex, d 3-9
11 Minimax Regret Approach An approach between the optimistic and the conservative Regret is the opportunity loss between the payoffs of the best decision given a state of nature and the decision you made
12 Regret Table State of Nature for Demand Decision Alternative Strong, s 1 Weak, s 2 Small Complex, d Medium Complex, d Large Complex, d
13 Minimax Regret Approach Select the minimum of the maximum regrets Decision Alternative Maximum Regrets Small Complex, d 1 12 Medium Complex, d 2 6 Large Complex, d 3 16 Minimum of the maximum regrets
14 Decision Making with Probabilities The expected value for an alternative N EV( di) = P( s j) Vij j= 1 N = Number of states of P(sj ) = the probability of nature the state of d i is defined as: nature s j
15 The Expected Value
16 The Expected Value
17 The Expected Value of Perfect Information If an expert could tell PDC the level of demand (either s 1 or s 2 ), what would that information be worth? Given the payoff table, PDC decision should be Expert says PDC Decision Demand is should be Strong, s 1 Large Complex, d 3 Payoff 20 Weak, s 2 Small Complex, d 1 7
18 Payoff Table State of Nature Decision Alternative Strong Demand s 1 Weak Demand s 2 Small Complex, d Medium Complex, d Large Complex, d
19 The Expected Value of Perfect Information The EV of the above strategy is referred to as the EV with perfect information and can be determined as: EVwPI = 0.8(20) + 0.2(7) = 17.4
20 The Expected Value of Perfect Information Without PI, the EV was previously determined as 14.2 and can be referred to as EVwoPI Hence, Expected Value of Perfect Information: EVPI = EVwoPI EVwPI = = 3.2
21 Risk Profile A risk profile is a graph showing the probability associated with each of the payoffs for a decision. A risk profile gives and indication of the degree of risk of a decision. It helps the decision maker to properly consider the risk and may lead to a decision other than that arrived at by the EV approach. Compare d 2 and d 3
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24 Sensitivity Analysis A study of how changes in value of one item, while maintaining the others constant, can effect the solution (decision selected) If the decision is changed with minor changes in the value of an item Decision is sensitive to the item Decision maker should try to obtain best estimate of value If the decision is not changed even with moderate changes in the value of an item Decision is not sensitive to the item Decision maker should not worry about obtaining better estimate of item.
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26 Expected Value as a Function of p EV Small, d1 Medium, d2 Large, d Probability
27 Decision Analysis with Sample Information Decision maker starts with initial probability assessments (prior probabilities) Often the decision maker will decide to do additional studies in order to arrive at better estimates of the probabilities (posterior probabilities) The new information is obtained through sampling from the population of concern and thus referred to as sample information.
28 PDC Decision Considering Sample Information PDC wishes to consider a market research study to improve its knowledge of the demand. The results of the study will be one of two: Favorable: indicating interest of a large number of people in purchasing condominiums Unfavorable: indicating interest of only few people in purchasing condominiums
29 Decision Trees
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31 Probabilities of Events Associated with Study Event Probabilities Favorable 0.77 Unfavorable 0.23 Strong Demand / Favorable 0.94 Weak Demand / Favorable 0.06 Strong Demand / Unfavorable 0.35 Weak Demand / Unfavorable 0.65
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37 0.8 Risk Profile for Optimal Decision Strategy 0.72 Probabilit Profit
38 Expected Value of & Efficiency of Sample Information EVSI = EVwSI EVwoSI EVwSI = EVwoSI = 14.2 EVSI = 1.73 Efficiency of Sample Information E(%) = EVSI / EVPI) x 100 E(%) = 1.73 / 3.2) x 100 = 54.1
39 Computing Brach Probabilities The Probabilities [P(s 1 /F), P(s 1 /U), P(s 2 /F)] etc. that were used to solve the tree, are referred to as posterior probabilities. In order to determine these probabilities we need to know the conditional probabilities of favorable or unfavorable report given the state of nature of a strong or weak demand; that is [P(F/s 1 ), P(U/s 1, P(F/s 2 ), P(U/s 2 )]. This can found from previous results of such studies
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41 Probabilities of Study Results Results of Market Research State of Nature Favorable, F Unfavorable, U Strong Demand, s 1 P(F/s 1 )=0.90 P(U/s 1 )= 0.10 Weak Demand, s 2 P(F/s 2 )=0.25 P(U/s 2 )=0.75
42 Branch Probabilities for Favorable Market State of Nature s j Prior Probability P(s j ) Prior Probability P(F s j ) Joint Probability P(F s j ) Posterior Probability P(s j F) s s P(F) =
43 Branch Probabilities for Unfavorable Market State of Nature s j Prior Probability P(s j ) Prior Probability P(U s j ) Joint Probability P(U s j ) Posterior Probability P(s j U) s s P(U) =
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